{"id":46829,"date":"2025-07-07T20:14:14","date_gmt":"2025-07-07T20:14:14","guid":{"rendered":"https:\/\/www.europesays.com\/us\/46829\/"},"modified":"2025-07-07T20:14:14","modified_gmt":"2025-07-07T20:14:14","slug":"uta-and-partners-use-ai-to-id-allergy-causing-pollen-for-smarter-health-and-planning-decisions-dallas-innovates","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/46829\/","title":{"rendered":"UTA and Partners Use AI to ID Allergy-Causing Pollen for Smarter Health and Planning Decisions \u00bb Dallas Innovates"},"content":{"rendered":"

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Behnaz Balmaki, assistant professor of research in biology at the University of Texas at Arlington, says pollen is a powerful indicator of ecosystem health and a key to tracking how plant communities respond to environmental change. [Photo: UTA]<\/p>\n

During peak pollen season, \u201cachoo\u201d is a familiar refrain for allergy sufferers across North Texas. But a new AI system developed by researchers at The University of Texas at Arlington, the University of Nevada, and Virginia Tech could help allergy alerts\u2014and even city planning\u2014get ahead of the sniffles.<\/strong><\/p>\n

Imagine trying to tell identical twins apart just by looking at their fingerprints. That\u2019s how tricky it can be for scientists to tell the difference between pollen grains from fir, spruce, and pine trees.<\/p>\n

Behnaz Balmaki, a biology researcher at UTA, said the findings could help urban planners make better choices about which trees to plant\u2014and where. <\/p>\n

Balmaki, who will soon take on a new role as an assistant professor of environmental science at Texas Woman\u2019s University, co-authored the study with Masoud Rostami from UTA\u2019s Division of Data Science and colleagues at the University of Nevada and Virginia Tech. Their work was published in Frontiers in Big Data.<\/p>\n

She said more detailed pollen data would be especially useful for schools, hospitals, parks, and neighborhoods. Health services could also tap the data to better time allergy warnings and treatment recommendations when pollen counts spike.<\/p>\n

\u201cPollen is a strong indicator of ecosystem health,\u201d Balmaki said.<\/p>\n

How the AI works<\/p>\n

UTA said the research team tested nine different deep-learning models on preserved samples from conifers, demonstrating that AI can improve classification speed and accuracy. The samples were preserved by the University of Nevada\u2019s Museum of Natural History.<\/p>\n

\u201cThis shows that deep learning can successfully support\u2014and even exceed\u2014traditional identification methods in speed and accuracy,\u201d Balmaki said.<\/p>\n

However, she noted that human expertise is still critical: good samples and ecological context are essential. \u201cThis isn\u2019t just about machines\u2014it\u2019s a collaboration between technology and science,\u201d she added.<\/p>\n

Ecosystem intelligence and agricultural potential<\/p>\n

Preserved pollen grains in lakebeds and peat bogs offer records of past plant communities, which can reveal how ecosystems have responded to environmental shifts over time.<\/p>\n

\u201cOur study shows deep-learning tools can significantly enhance the speed and accuracy of pollen classification,\u201d Balmaki said.<\/p>\n

She said that opens the door to large-scale environmental monitoring, detailed reconstructions of ecological change, and better allergen tracking.<\/p>\n

\u201cIt also holds promise for improving allergen tracking by identifying exactly which species are releasing pollen and when.\u201d<\/p>\n

Balmaki added that shifts in pollen composition can signal changes<\/p>\n

Balmaki said shifts in pollen composition can signal changes in vegetation, moisture, or even past fire activity. Farmers could use that data to track trends that affect crops and soil conditions.<\/p>\n

UTA said the system could also support wildlife and pollinator conservation.<\/p>\n

\u201cBy identifying which plant species are present or declining in an area, we can better understand how these changes impact the entire food web and take steps to protect critical relationships between plants and pollinators,\u201d Balmaki said.<\/p>\n

What\u2019s next<\/p>\n

The university said that for future projects, Balmaki and her collaborators plan to expand their research to include a wider range of plant species. The goal is to develop a comprehensive pollen identification system that can be applied across regions in the United States to better understand how plant communities may shift in response to extreme weather events.<\/p>\n

Editor\u2019s note: This story was updated on July 7, 2025, at 2:34 p.m. to include Balmaki\u2019s new appointment at Texas Woman\u2019s University.<\/p>\n

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